Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin

Shinya Sugita; Tim Parshall; Randy Calcote; Karen Walker

doi:10.1016/j.yqres.2010.07.008

Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin

Shinya Sugita, Tim Parshall, Randy Calcote, Karen Walker

Earth and Environmental Sciences-Twin Cities

Research output: Contribution to journal › Article › peer-review

116 Scopus citations

Abstract

The Landscape Reconstruction Algorithm (LRA) overcomes some of the fundamental problems in pollen analysis for quantitative reconstruction of vegetation. LRA first uses the REVEALS model to estimate regional vegetation using pollen data from large sites and then the LOVE model to estimate vegetation composition within the relevant source area of pollen (RSAP) at small sites by subtracting the background pollen estimated from the regional vegetation composition. This study tests LRA using training data from forest hollows in northern Michigan (35 sites) and northwestern Wisconsin (43 sites). In northern Michigan, surface pollen from 152-ha and 332-ha lakes is used for REVEALS. Because of the lack of pollen data from large lakes in northwestern Wisconsin, we use pollen from 21 hollows randomly selected from the 43 sites for REVEALS. RSAP indirectly estimated by LRA is comparable to the expected value in each region. A regression analysis and permutation test validate that the LRA-based vegetation reconstruction is significantly more accurate than pollen percentages alone in both regions. Even though the site selection in northwestern Wisconsin is not ideal, the results are robust. The LRA is a significant step forward in quantitative reconstruction of vegetation.

Original language	English (US)
Pages (from-to)	289-300
Number of pages	12
Journal	Quaternary Research
Volume	74
Issue number	2
DOIs	https://doi.org/10.1016/j.yqres.2010.07.008
State	Published - Sep 2010

Bibliographical note

Funding Information:
We are grateful to M.B. Davis for her enthusiastic support and intellectual stimuli on the subject and project. P. Nacionales provided invaluable technical support for data management. M.B. Davis and Ngyen Le collected the surface sediment samples from Clark and Loon Lakes in Sylvania Wilderness. Critical comments on an earlier version of the manuscript from M.B. Davis, B. Odgaard, F. Mazier and two anonymous reviewers improved the manuscript greatly. This project was partially supported by the National Science Foundation ( BSR8615196 , BSR8916503 , DEB9221371 , DEB 9221375 and ATM 97-09633 ), the NSF REU program , and the Mellon Foundation . Sugita was also partially supported by funding from the College of Biological Sciences, University of Minnesota , and the Estonian Science Foundation Mobilitas Programme ( MTT3 ) and Target Financed Project ( SF 0280016 S07 ).

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

LOVE
Landscape Reconstruction Algorithm
Pollen analysis
Quantitative reconstruction of vegetation
REVEALS
Relevant source area of pollen

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title = "Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin",

abstract = "The Landscape Reconstruction Algorithm (LRA) overcomes some of the fundamental problems in pollen analysis for quantitative reconstruction of vegetation. LRA first uses the REVEALS model to estimate regional vegetation using pollen data from large sites and then the LOVE model to estimate vegetation composition within the relevant source area of pollen (RSAP) at small sites by subtracting the background pollen estimated from the regional vegetation composition. This study tests LRA using training data from forest hollows in northern Michigan (35 sites) and northwestern Wisconsin (43 sites). In northern Michigan, surface pollen from 152-ha and 332-ha lakes is used for REVEALS. Because of the lack of pollen data from large lakes in northwestern Wisconsin, we use pollen from 21 hollows randomly selected from the 43 sites for REVEALS. RSAP indirectly estimated by LRA is comparable to the expected value in each region. A regression analysis and permutation test validate that the LRA-based vegetation reconstruction is significantly more accurate than pollen percentages alone in both regions. Even though the site selection in northwestern Wisconsin is not ideal, the results are robust. The LRA is a significant step forward in quantitative reconstruction of vegetation.",

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author = "Shinya Sugita and Tim Parshall and Randy Calcote and Karen Walker",

note = "Funding Information: We are grateful to M.B. Davis for her enthusiastic support and intellectual stimuli on the subject and project. P. Nacionales provided invaluable technical support for data management. M.B. Davis and Ngyen Le collected the surface sediment samples from Clark and Loon Lakes in Sylvania Wilderness. Critical comments on an earlier version of the manuscript from M.B. Davis, B. Odgaard, F. Mazier and two anonymous reviewers improved the manuscript greatly. This project was partially supported by the National Science Foundation ( BSR8615196 , BSR8916503 , DEB9221371 , DEB 9221375 and ATM 97-09633 ), the NSF REU program , and the Mellon Foundation . Sugita was also partially supported by funding from the College of Biological Sciences, University of Minnesota , and the Estonian Science Foundation Mobilitas Programme ( MTT3 ) and Target Financed Project ( SF 0280016 S07 ). Copyright: Copyright 2011 Elsevier B.V., All rights reserved.",

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Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin

Abstract

Bibliographical note

Keywords

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